Two precision medicine predictive tools for six malignant solid tumors: from gene-based research to clinical application

RNA in situ conformation sequencing reveals novel long-range RNA structures with impact on splicing

Over recent years, long-range RNA structure has emerged as a factor that is fundamental to alternative splicing regulation. An increasing number of human disorders are now being associated with splicing defects; hence it is essential to develop methods that assess long-range RNA structure experimentally. RNA in situ conformation sequencing (RIC-seq) is a method that recapitulates RNA structure within physiological RNA-protein complexes. In this work, we juxtapose pairs of conserved complementary regions (PCCRs) that were predicted in silico with the results of RIC-seq experiments conducted in seven human cell lines. We show statistically that RIC-seq support of PCCRs correlates with their properties, such as equilibrium free energy, presence of compensatory substitutions, and occurrence of A-to-I RNA editing sites and forked eCLIP peaks. Exons enclosed in PCCRs that are supported by RIC-seq tend to have weaker splice sites and lower inclusion rates, which is indicative of post-transcriptional splicing regulation mediated by RNA structure. Based on these findings, we prioritize PCCRs according to their RIC-seq support and show, using antisense nucleotides and minigene mutagenesis, that PCCRs in two disease-associated human genes, PHF20L1 and CASK, and also PCCRs in their murine orthologs, impact alternative splicing. In sum, we demonstrate how RIC-seq experiments can be used to discover functional long-range RNA structures, and particularly those that regulate alternative splicing.

Gut microbiome and nutrition-related predictors of response to immunotherapy in cancer: making sense of the puzzle

The gut microbiome is emerging as an important predictor of response to immune checkpoint inhibitor (ICI) therapy for patients with cancer. However, several nutrition-related patient characteristics, which are themselves associated with changes in gut microbiome, are also prognostic markers for ICI treatment response and survival. Thus, increased abundance of Akkermansia muciniphila, Phascolarctobacterium, Bifidobacterium and Rothia in stool are consistently associated with better response to ICI treatment. A. muciniphila is also more abundant in stool in patients with higher muscle mass, and muscle mass is a strong positive prognostic marker in cancer, including after ICI treatment. This review explores the complex inter-relations between the gut microbiome, diet and patient nutritional status and the correlations with response to ICI treatment. Different multivariate approaches, including archetypal analysis, are discussed to help identify the combinations of features which may select patients most likely to respond to ICI treatment.

Comprehensive analysis of m6A circRNAs identified in colorectal cancer by MeRIP sequencing

Purpose

To characterize the entire profile of m6A modifications and differential expression patterns for circRNAs in colorectal cancer (CRC).

Methods

First, High-throughput MeRIP-sequencing and RNA-sequencing was used to determine the difference in m6A methylome and expression of circRNA between CRC tissues and tumor-adjacent normal control (NC) tissues. Then, GO and KEGG analysis detected pathways involved in differentially methylated and differentially expressed circRNAs (DEGs). The correlations between m6A status and expression level were calculated using a Pearson correlation analysis. Next, the networks of circRNA-miRNA-mRNA were visualized using the Target Scan and miRanda software. Finally, We describe the relationship of distance between the m6A peak and internal ribosome entry site (IRES) and protein coding potential of circRNAs.

Results

A total of 4340 m6A peaks of circRNAs in CRC tissue and 3216 m6A peaks of circRNAs in NC tissues were detected. A total of 2561 m6A circRNAs in CRC tissues and 2129 m6A circRNAs in NC tissues were detected. Pathway analysis detected that differentially methylated and expressed circRNAs were closely related to cancer. The conjoint analysis of MeRIP-seq and RNA-seq data discovered 30 circRNAs with differentially m6A methylated and synchronously differential expression. RT-qPCR showned circRNAs (has_circ_0032821, has_circ_0019079, has_circ_0093688) were upregulated and circRNAs (hsa_circ_0026782, hsa_circ_0108457) were downregulated in CRC. In the ceRNA network, the 10 hyper-up circRNAs were shown to be associated with 19 miRNAs and regulate 16 mRNAs, 14 hypo-down circRNAs were associated with 30 miRNAs and regulated 27 mRNAs. There was no significant correlation between the level of m6A and the expression of circRNAs. The distance between the m6A peak and IRES was not significantly related to the protein coding potential of circRNAs.

Conclusion

Our study found that there were significant differences in the m6A methylation patterns of circRNAs between CRC and NC tissues. M6A methylation may affect circRNA-miRNA-mRNA co-expression in CRC and further affect the regulation of cancer-related target genes.

Improve individual treatment by comparing treatment benefits: cancer artificial intelligence survival analysis system for cervical carcinoma

Purpose

The current study aimed to construct a novel cancer artificial intelligence survival analysis system for predicting the individual mortality risk curves for cervical carcinoma patients receiving different treatments.

Methods

Study dataset (n = 14,946) was downloaded from Surveillance Epidemiology and End Results database. Accelerated failure time algorithm, multi-task logistic regression algorithm, and Cox proportional hazard regression algorithm were used to develop prognostic models for cancer specific survival of cervical carcinoma patients.

Results

Multivariate Cox regression identified stage, PM, chemotherapy, Age, PT, and radiation_surgery as independent influence factors for cervical carcinoma patients. The concordance indexes of Cox model were 0.860, 0.849, and 0.848 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.881, 0.845, and 0.841 in validation dataset. The concordance indexes of accelerated failure time model were 0.861, 0.852, and 0.851 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.882, 0.847, and 0.846 in validation dataset. The concordance indexes of multi-task logistic regression model were 0.860, 0.863, and 0.861 for 12-month, 36-month, and 60-month in model dataset, whereas it were 0.880, 0.860, and 0.861 in validation dataset. Brier score indicated that these three prognostic models have good diagnostic accuracy for cervical carcinoma patients. The current research lacked independent external validation study.

Conclusion

The current study developed a novel cancer artificial intelligence survival analysis system to provide individual mortality risk predictive curves for cervical carcinoma patients based on three different artificial intelligence algorithms. Cancer artificial intelligence survival analysis system could provide mortality percentage at specific time points and explore the actual treatment benefits under different treatments in four stages, which could help patient determine the best individualized treatment. Cancer artificial intelligence survival analysis system was available at: https://zhangzhiqiao15.shinyapps.io/Tumor_Artificial_Intelligence_Survival_Analysis_System/.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12967-022-03491-8.

Artificial intelligence predictive system of individual survival rate for lung adenocarcinoma

Background

The current research aimed to develop an artificial intelligence predictive system for individual survival rate of lung adenocarcinoma (LUAD).

Methods

Independent risk variables were identified by multivariate Cox regression. Artificial intelligence predictive system was constructed using three different data mining algorithms.

Results

Stage, PM, chemotherapy, PN, age, PT, sex, and radiation_surgery were determined as risk factors for LUAD patients. For 12-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.852, 0.821, and 0.835, respectively. For 36-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.901, 0.864, and 0.862, respectively. For 60-month survival rate in model cohort, concordance indexes of RFS, MTLR, and Cox models were 0.899, 0.874, and 0.866, respectively. The concordance indexes in validation dataset were similar to those in model dataset.

Conclusions

The current study designed an individualized survival predictive system, which could provide individual survival curves using three different artificial intelligence algorithms. This artificial intelligence predictive system could directly convey treatment benefits by comparing individual mortality risk curves under different treatments. This artificial intelligence predictive tool is available at https://zhangzhiqiao11.shinyapps.io/Artificial_Intelligence_Survival_Prediction_System_AI_E1001/.

Bioinformatics analysis reveals immune prognostic markers for overall survival of colorectal cancer patients: a novel machine learning survival predictive system

Objectives

Immune microenvironment was closely related to the occurrence and progression of colorectal cancer (CRC). The objective of the current research was to develop and verify a Machine learning survival predictive system for CRC based on immune gene expression data and machine learning algorithms.

Methods

The current study performed differentially expressed analyses between normal tissues and tumor tissues. Univariate Cox regression was used to screen prognostic markers for CRC. Prognostic immune genes and transcription factors were used to construct an immune-related regulatory network. Three machine learning algorithms were used to create an Machine learning survival predictive system for CRC. Concordance indexes, calibration curves, and Brier scores were used to evaluate the performance of prognostic model.

Results

Twenty immune genes (BCL2L12, FKBP10, XKRX, WFS1, TESC, CCR7, SPACA3, LY6G6C, L1CAM, OSM, EXTL1, LY6D, FCRL5, MYEOV, FOXD1, REG3G, HAPLN1, MAOB, TNFSF11, and AMIGO3) were recognized as independent risk factors for CRC. A prognostic nomogram was developed based on the previous immune genes. Concordance indexes were 0.852, 0.778, and 0.818 for 1-, 3- and 5-year survival. This prognostic model could discriminate high risk patients with poor prognosis from low risk patients with favorable prognosis.

Conclusions

The current study identified twenty prognostic immune genes for CRC patients and constructed an immune-related regulatory network. Based on three machine learning algorithms, the current research provided three individual mortality predictive curves. The Machine learning survival predictive system was available at: https://zhangzhiqiao8.shinyapps.io/Artificial_Intelligence_Survival_Prediction_for_CRC_B1005_1/, which was valuable for individualized treatment decision before surgery.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-022-04657-3.

Analysis of lncRNAs and mRNA Expression in the ZBTB1 Knockout Monoclonal EL4 Cell Line and Combined Analysis With miRNAs and circRNAs

In previous experiments, we identified the effect of deletion of the Zbtb1 gene on circRNAs and microRNAs. In this study, we examined the expression profiles of lncRNAs and mRNAs using the RNA-seq method for Zbtb1-deficient EL4 cells and performed a clustering analysis of differentially expressed lncRNAs and mRNAs. GO term histograms and KEGG scatter plots were drawn. For the experimental results, a joint analysis was performed, which predicted the regulatory relationships among lncRNAs, mRNAs, microRNAs and circRNAs. For the regulatory relationship between lncRNAs and target genes, the chromatin structure and the degree of openness were verified for the possible target gene locations regulated by lncRNA using experimental methods such as Hi-C and ATAC-seq. Ultimately, the possible differential regulation of the Brcal and Dennd5d genes by lncRNAs and the differential changes in transcription factor binding sites in the promoter region were identified. For neRNA-regulated target genes with significantly differentially expressed mRNAs, a combined screen was performed, and the final obtained candidate target genes were subjected to GO and KEGG term enrichment analyses. Our results illustrate that the Zbtb1 gene can not only function as a regulatory factor but also regulate EL4 cells from multiple perspectives based on ceRNA theory.

CoSMeD: a user-friendly web server to estimate 5-year survival probability of left-sided and right-sided colorectal cancer patients using molecular data

SUMMARY

Colorectal cancer is a heterogeneous disease with diverse prognoses between left-sided and right-sided patients; therefore, it is necessary to precisely evaluate the survival probability of side-specific colorectal cancer patients. Here, we collected multi-omics data from The Cancer Genome Atlas program, including gene expression, DNA methylation and microRNA expression. Specificity measure and robust likelihood-based survival analysis were used to identify 6 left-sided and 28 right-sided prognostic biomarkers. Compared to the performance of clinical prognostic models, the addition of these biomarkers could significantly improve the discriminatory ability and calibration in predicting side-specific 5-year survival for colorectal cancer. Additional dataset derived from Gene Expression Omnibus was used to validate the prognostic value of side-specific genes. Finally, we constructed colorectal cancer side-specific molecular database (CoSMeD), a user-friendly interface for estimating side-specific colorectal cancer 5-year survival probability, which can lay the basis for personalized management of left-sided and right-sided colorectal cancer patients.

AVAILABILITY AND IMPLEMENTATION

CoSMeD is freely available at https://mulongdu.shinyapps.io/cosmed.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

ceRNAs in Cancer: Mechanism and Functions in a Comprehensive Regulatory Network

Noncoding RNAs have been shown with powerful ability in post-transcriptional regulation, enabling intertwined RNA crosstalk and global molecular interaction in a large amount of dysfunctional conditions including cancer. Competing endogenous RNAs (ceRNAs) are those competitively binding with shared microRNAs (miRNAs), freeing their counterparts from miRNA-induced degradation, thus actively influencing and connecting with each other. Constantly updated analytical approaches boost outstanding advancement achieved in this burgeoning hotspot in multilayered intracellular communication, providing new insights into pathogenesis and clinical treatment. Here, we summarize the mechanisms and correlated factors under this RNA interplay and deregulated transcription profile in neoplasm and tumor progression, underscoring the great significance of ceRNAs for diagnostic values, monitoring biomarkers, and prognosis evaluation in cancer.

High expression of DNA damage-inducible transcript 4 (DDIT4) is associated with advanced pathological features in the patients with colorectal cancer

DNA damage-inducible transcript 4 (DDIT4) is induced in various cellular stress conditions. This study was conducted to investigate expression and prognostic significance of DDIT4 protein as a biomarker in the patients with colorectal cancer (CRC). PPI network and KEGG pathway analysis were applied to identify hub genes among obtained differentially expressed genes in CRC tissues from three GEO Series. In clinical, expression of DDIT4 as one of hub genes in three subcellular locations was evaluated in 198 CRC tissues using immunohistochemistry method on tissue microarrays. The association between DDIT4 expression and clinicopathological features as well as survival outcomes were analyzed. Results of bioinformatics analysis indicated 14 hub genes enriched in significant pathways according to KEGG pathways analysis among which DDIT4 was selected to evaluate CRC tissues. Overexpression of nuclear DDIT4 protein was found in CRC tissues compared to adjacent normal tissues (P = 0.003). Furthermore, higher nuclear expression of DDIT4 was found to be significantly associated with the reduced tumor differentiation and advanced TNM stages (all, P = 0.009). No significant association was observed between survival outcomes and nuclear expression of DDIT4 in CRC cases. Our findings indicated higher nuclear expression of DDIT4 was significantly associated with more aggressive tumor behavior and more advanced stage of disease in the patients with CRC.

Competing endogenous RNA network identifies mRNA biomarkers for overall survival of lung adenocarcinoma: two novel on-line precision medicine predictive tools

Background

Individual mortality risk predicted curve at the individual level can provide valuable information for directing individual treatment decision. The present study attempted to explore potential post-transcriptional biological regulatory mechanism related with overall survival of lung adenocarcinoma (LUAD) patients through competitive endogenous RNA (ceRNA) network and develop two precision medicine predictive tools for predicting the individual mortality risk curves for overall survival of LUAD patients.

Methods

Multivariable Cox regression analyses were performed to explore the potential prognostic indicators, which were used to construct a prognostic model for overall survival of LUAD patients. Time-dependent receiver operating characteristic (ROC) curves were used to assess the predictive performance of prognostic model.

Results

There were 494 LUAD patients in model cohort and 233 LUAD patients in validation cohort. Differentially expressed mRNAs, miRNAs, and lncRNAs were identified between LUAD tissues and normal tissues. A ceRNA regulatory network was constructed on previous differentially expressed mRNAs, miRNAs, and lncRNAs. Fourteen mRNA biomarkers were identified as independent risk factors by multivariate Cox regression and used to develop a prognostic model for overall survival of LUAD patients. The C-indexes of prognostic model in model group were 0.786 (95% CI [0.744–0.828]), 0.736 (95% CI [0.694–0.778]) and 0.766 (95% CI [0.724–0.808]) for one year, two year and three year overall survival respectively. Two precision medicine predicted tools were developed for predicting individual mortality risk curves for LUAD patients.

Conclusion

The current study explored potential post-transcriptional biological regulatory mechanism and prognostic biomarkers for overall survival of LUAD patients. Two on-line precision medicine predictive tools were helpful to predict the individual mortality risk predicted curves for overall survival of LUAD patients. Smart Cancer Survival Predictive System could be used at https://zhangzhiqiao2.shinyapps.io/Smart_cancer_predictive_system_9_LUAD_E1002/.

The Prognosis Value of PSPC1 Expression in Nasopharyngeal Cancer

Background

Paraspeckle component 1 (PSPC1) is overexpressed in various cancer and correlated with poor survival in the patients. However, little is known about its expression and role in the progression of nasopharyngeal carcinomas (NPC). The purpose of this study is to examine PSPC1 expression in NPC and explore its role in clinical prognosis of radiation therapy.

Methods

The association of PSPC1 expression with clinicopathological features of 109 NPC patients was examined using partial correlation analysis. Cancer tissues were obtained prior to clinical treatment. All cases were diagnosed and pathologically confirmed to be poorly differentiated or undifferentiated NPC without distant metastasis. The patients were then treated with radiation and followed-up. Survival analysis was performed.

Results

Partial correlation analysis revealed that the PSPC1 expression in NPC was correlated with N classification, recurrence, prognosis and radiosensitivity in NPC patients, but not with the gender, age, pathohistological pattern, clinical stage, and T classification. The overexpression of PSPC1 was detected in 64 samples (58.72%). Kaplan–Meier survival analysis revealed that the overall survival (OS) was longer in NPC patients with PSPC1 low expression than that in those with PSPC1 high expression. Moreover, patients with the overexpression of PSPC1 had a low progression-free survival and distant metastasis-free survival rate, compared to those who had a low expression of PSPC1. Although not statistically significant, patients with high expression of PSPC1 had a lower locoregional recurrence-free survival rate than those with low expression, and the curves between the two groups was well separated.

Conclusion

PSPC1 overexpression was associated with poor prognosis for NPC, which might be a novel useful biomarker to predict the response of NPC to radiation therapy and its clinical outcome.

Immune gene prognostic signature for disease free survival of gastric cancer: Translational research of an artificial intelligence survival predictive system

The progress of artificial intelligence algorithms and massive data provide new ideas and choices for individual mortality risk prediction for cancer patients. The current research focused on depict immune gene related regulatory network and develop an artificial intelligence survival predictive system for disease free survival of gastric cancer.

Multi-task logistic regression algorithm, Cox survival regression algorithm, and Random survival forest algorithm were used to develop the artificial intelligence survival predictive system.

Nineteen transcription factors and seventy immune genes were identified to construct a transcription factor regulatory network of immune genes. Multivariate Cox regression identified fourteen immune genes as prognostic markers. These immune genes were used to construct a prognostic signature for gastric cancer. Concordance indexes were 0.800, 0.809, and 0.856 for 1-, 3- and 5- year survival. An interesting artificial intelligence survival predictive system was developed based on three artificial intelligence algorithms for gastric cancer. Gastric cancer patients with high risk score have poor survival than patients with low risk score.

The current study constructed a transcription factor regulatory network and developed two artificial intelligence survival prediction tools for disease free survival of gastric cancer patients. These artificial intelligence survival prediction tools are helpful for individualized treatment decision.

PDIA4 Correlates with Poor Prognosis and is a Potential Biomarker in Glioma

Purpose

Gliomas, characterized by aggressiveness and invasiveness, remain incurable after conventional therapies. The molecular mechanisms driving the progression and maintenance of glioma are still poorly understood.

Methods

The TCGA and CGGA databases were chosen for bioinformatics analysis. Gene expression profiling interactive analysis (GEPIA) was performed for differential analysis. The Kaplan–Meier method was chosen for survival analysis. Analysis of stromal and immune infiltration was performed using the ESTIMATE algorithm and xCell package. qPCR and Western blotting were performed to measure the expression of PDIA4 at the mRNA and protein levels. IHC was performed to detect the expression of PDIA4 in glioma tissues. The viability of glioma cells was evaluated by the CCK8 assay.

Results

In this study, we identified high PDIA4 expression in gliomas that correlated with poor prognosis. The association between IDH1 and different glioma patterns also indicated the potential biological role of PDIA4 in tumor development. Mechanistically, PDIA4 interacted with multiple immunological components to promote an immunosuppressive tumor microenvironment (TME). Knockdown of PDIA4 significantly impaired the proliferation of GBM cells.

Conclusion

Our results confirm that PDIA4 is an efficient biomarker of gliomas, with clinical implications for prognosis and therapeutic strategies.

Competing endogenous RNAs and cancer: How coding and non-coding molecules cross-talk can impinge on disease

Cancers are characterized by several dramatic biological changes. Among the many post-transcriptional regulatory mechanisms, microRNAs are known as fine-tune regulators for their transcript silencing ability. Competing endogenous RNAs (ceRNAs) are transcripts that share microRNA binding elements and can compete for them, thus regulating each other indirectly. ceRNA networks interconnect the regulatory control of different transcript classes of the coding and non-coding space and co-operate with other cellular and molecular regulatory mechanisms. Altered ceRNA networks are involved in tumor formation and progression as well as in chemoresistance, in invasion and in the onset of metastases. The analysis of changes in the balance between ceRNA transcripts could offer hints to identify novel pathways for diagnosis, prognosis and therapies in precision medicine interventions. Moreover, the possibility to query highly specific tumor databases, such as TCGA, and to combine clinical data, transcript expression and sequence information is allowing to develop specific predictive tools for precision medicine.

Competing Endogenous RNAs, Non-Coding RNAs and Diseases: An Intertwined Story

MicroRNAs (miRNAs), a class of small non-coding RNA molecules, are responsible for RNA silencing and post-transcriptional regulation of gene expression. They can mediate a fine-tuned crosstalk among coding and non-coding RNA molecules sharing miRNA response elements (MREs). In a suitable environment, both coding and non-coding RNA molecules can be targeted by the same miRNAs and can indirectly regulate each other by competing for them. These RNAs, otherwise known as competing endogenous RNAs (ceRNAs), lead to an additional post-transcriptional regulatory layer, where non-coding RNAs can find new significance. The miRNA-mediated interplay among different types of RNA molecules has been observed in many different contexts. The analyses of ceRNA networks in cancer and other pathologies, as well as in other physiological conditions, provide new opportunities for interpreting omics data for the field of personalized medicine. The development of novel computational tools, providing putative predictions of ceRNA interactions, is a rapidly growing field of interest. In this review, I discuss and present the current knowledge of the ceRNA mechanism and its implications in a broad spectrum of different pathologies, such as cardiovascular or autoimmune diseases, cancers and neurodegenerative disorders.

The Influence of Salinomycin on the Expression Profile of mRNAs Encoding Selected Caspases and MiRNAs Regulating their Expression in Endometrial Cancer Cell Line

BACKGROUND

Apoptosis could take place in the pathway dependent on death receptors or pathways dependent on mitochondria. In both, a key role is played by enzymes with protease activity, known as caspases.

AIM

The aim of this study was to assess the variances in the expression pattern of caspase-dependent signaling pathways in the endometrial cancer cell line when treated with salinomycin. Additionally, the changes in the level of miRNA that potentially regulate these mRNAs were evaluated.

MATERIALS AND METHODS

Endometrial cancer cells were treated with 1 μM of salinomycin for 12, 24 and 48 hours. Untreated cells made up the control culture. The molecular analysis consisted of screening mRNA and miRNA microarray expression profiles of caspases, and the evaluation of the expression of caspases 3,8 and 9 by RTqPCR, also on the protein level.

RESULTS AND DISCUSSION

It was observed that 5 of the 14 differentiating mRNAs were commonly found for all incubation times of the cells and they corresponded with CASP3, CASP8, and CASP9 genes. The highest impact probability was determined between CASP3(up-regulated) and hsa- miR- 30d (FC -2.01), CASP8 (down-regulated) and hsa-miR-21 (FC +1.39) and between CASP9 (upregulated) and hsa-miR-1271 (FC +1.71).

CONCLUSION

Salinomycin induces the apoptosis of endometrial cancer cells. The largest increase in activity was noted for caspases 3 and 9, while the expression of caspase 8 was decreased. Salinomycin causes a regulatory effect on the transcriptomes of mRNA and miRNA in in vitro endometrial cancer cells.